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Feature Extraction for Facial Expression Recognition based on Hybrid Face RegionsLAJEVARDI, S.M. , HUSSAIN, Z. M.
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facial expression recognition, Gabor filters, face regions, human computer interaction, feature extraction
recognition(19), facial(18), lajevardi(8), gabor(7), pattern(6), image(6), hussain(5), neural(4), features(4), feature(4)
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About this article
Date of Publication: 2009-10-26
Volume 9, Issue 3, Year 2009, On page(s): 63 - 67
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2009.03012
Web of Science Accession Number: 000271872000012
SCOPUS ID: 77954728504
Facial expression recognition has numerous applications, including psychological research, improved human computer interaction, and sign language translation. A novel facial expression recognition system based on hybrid face regions (HFR) is investigated. The expression recognition system is fully automatic, and consists of the following modules: face detection, facial detection, feature extraction, optimal features selection, and classification. The features are extracted from both whole face image and face regions (eyes and mouth) using log Gabor filters. Then, the most discriminate features are selected based on mutual information criteria. The system can automatically recognize six expressions: anger, disgust, fear, happiness, sadness and surprise. The selected features are classified using the Naive Bayesian (NB) classifier. The proposed method has been extensively assessed using Cohn-Kanade database and JAFFE database. The experiments have highlighted the efficiency of the proposed HFR method in enhancing the classification rate.
|References|||||Cited By «-- Click to see who has cited this paper|
| Kanade, T., Cohn, J. F., and Tian, Y., "Comprehensive database for facial expression analysis", Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition, Grenoble, France, pp. 46-53, 2000
 Kaliouby, R. E., Robinson, P., "Real-time inference of complex mental states from facial expressions and head gestures", Conference on Computer Vision and Pattern Recognition Workshop, vol. 3, pp. 181-200, 2004
 Tian, Y., Kanade, T., Cohn, J. F., "Recognizing action units for facial expression analysis", IEEE Tran. on Pattern Analysis and Machine Intelligence, vol. 23, no. 2, pp. 97-115, 2001
[CrossRef] [Web of Science Times Cited 552] [SCOPUS Times Cited 810]
 Viola, P., Jones, M., "Robust real-time object detection", International Journal of Computer Vision, 57(2), pp. 137-154, 2004
[CrossRef] [Web of Science Times Cited 5107] [SCOPUS Times Cited 7036]
 Guyon, I., Gunn, S., Nikravesh, M., Zadeh, A., "Feature Extraction Foundations and Applications", Springer, 2006
 Lyons, M., Akamatsu, S., Kamachi, M., and Gyoba, J., "Coding facial expressions with Gabor wavelets", In FG'98: Proceedings of the 3rd International Conference on Face and Gesture Recognition, Washington, USA, 1998
 Zheng, D., Zhao, Y., Wang, J., "Features extraction using a Gabor filter family", Proceedings of the Sixth IASTED International Conference Signal and Image Processing, Hawaii, USA, 2004
 Rish, I., "An empirical study of the naive Bayes classifier", IJCAI Workshop on Empirical Methods in Artificial Intelligence, vol. 335, pp. 41-46, 2001
 Claude, F. B., Chibelushi, C., "Facial Expression Recognition: A Brief Tutorial Overview", 2003
 Battiti, R., "Using mutual information for selecting features in supervised neural net learning", IEEE Trans. on Neural Networks, vol. 5, no. 4, pp. 537-550, 1994
[CrossRef] [PubMed] [Web of Science Times Cited 784] [SCOPUS Times Cited 1212]
 Liu, F., Wang, Z., Wang, L., Meng, X., "Facial expression recognition using HLAC features and WPCA", Lecture Notes in Computer Science, Springer, 2005
 Buciu, I., Kotropoulos, C., and Pitas, I., "ICA and Gabor representation for facial expression recognition", International Conference on Image Processing, vol. 2, pp. 14-17, 2003
 Field, D.J., "Relations between the images and the response properties of cortical cells", Jour. of the Optical Society of America, pp. 2379-2394, 1987
[CrossRef] [Web of Science Times Cited 1716] [SCOPUS Times Cited 1969]
 Duda, R. O., Hart, P. E., Stork, D. G., "Pattern Classification", Wiley, New York, 2001
 Park, S., and Kim, D., "Subtle facial expression recognition using motion magnification", Pattern Recognition Letters, 30(7), pp. 708-716, 2009
[CrossRef] [Web of Science Times Cited 22] [SCOPUS Times Cited 26]
 Xie, X., and Lam, K.M., "Facial expression recognition based on shape and texture", Pattern Recognition, 42(5), pp. 1003-1011, 2009
[CrossRef] [Web of Science Times Cited 32] [SCOPUS Times Cited 46]
 Kotsia, I., Zafeiriou, S., and Pitas, I., "Novel multiclass classifiers based on the minimization of the within-class variance", IEEE Tran. on Neural Networks, 20(1), pp. 14-34, 2009
[CrossRef] [Web of Science Times Cited 23] [SCOPUS Times Cited 26]
 Geetha, A., Ramalingam, V., Palanivel, S., Palaniappan, B., "Facial expression recognition: a real time approach", Expert Systems with Applications, 36(1), pp. 303-308, 2009
[CrossRef] [Web of Science Times Cited 25] [SCOPUS Times Cited 35]
 Lajevardi, S. M., Lech, M., "Facial Expression Recognition Using Neural Networks and Log-Gabor Filters", Proceedings of Digital Image Computing: Techniques and Applications (DICTA'08), pp. 77-83, Australia, 2008
[CrossRef] [SCOPUS Times Cited 23]
 Lajevardi, S. M., Lech, M., "Averaged Gabor filter features for facial expression recognition", Proceedings of Digital Image Computing: Techniques and Applications (DICTA'08), pp. 71-76, Australia, 2008
[CrossRef] [SCOPUS Times Cited 22]
 Lajevardi, S. M., Lech, M., "Facial expression recognition from image sequences using optimised feature selection", 23rd International Conference on Image and Vision Computing (IVCNZ'08), pp. 1-6, New Zealand, 2008
[CrossRef] [SCOPUS Times Cited 20]
 Lajevardi, S. M., Hussain, Z. M., "Facial expression recognition: Gabor filters versus higher-order correlators", International Conference on Communication, Computer and Power (ICCCP'08), pp. 354-358, Oman, 2009
 Lajevardi, S. M., Hussain, Z. M., "Facial expression recognition using log-Gabor filters and local binary pattern operators", International Conference on Communication, Computer and Power (ICCCP'08), pp. 349-353, Oman, 2009
 Lajevardi, S. M., Hussain, Z. M., "Zernike moments for facial expression recognition", International Conference on Communication, Computer and Power (ICCCP'08), pp. 371-381, Oman, 2009
 Lajevardi, S. M., Hussain, Z. M., "Feature selection for facial expression recognition based on mutual information", IEEE-GCC'09 Conference, Kuwait, 2009
 Lajevardi, S. M., Hussain, Z. M., "Feature selection for facial expression recognition based on optimization algorithm", Second International Workshop on Nonlinear Dynamics and Synchronization (INDS'09), Klagenfurt, Austria, 2009
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